Aerial-trained deep learning networks for surveying cetaceans from satellite imagery

Autoři: Alex Borowicz aff001;  Hieu Le aff002;  Grant Humphries aff004;  Georg Nehls aff005;  Caroline Höschle aff005;  Vladislav Kosarev aff005;  Heather J. Lynch aff001
Působiště autorů: Department of Ecology & Evolution, Stony Brook University, Stony Brook, New York, United States of America aff001;  Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York, United States of America aff002;  Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America aff003;  HiDef Aerial Surveying Ltd., Cleator Moor, Cumbria, United Kingdom aff004;  BioConsult SH GmbH & Co. KG, Husum, Germany aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


Most cetacean species are wide-ranging and highly mobile, creating significant challenges for researchers by limiting the scope of data that can be collected and leaving large areas un-surveyed. Aerial surveys have proven an effective way to locate and study cetacean movements but are costly and limited in spatial extent. Here we present a semi-automated pipeline for whale detection from very high-resolution (sub-meter) satellite imagery that makes use of a convolutional neural network (CNN). We trained ResNet, and DenseNet CNNs using down-scaled aerial imagery and tested each model on 31 cm-resolution imagery obtained from the WorldView-3 sensor. Satellite imagery was tiled and the trained algorithms were used to classify whether or not a tile was likely to contain a whale. Our best model correctly classified 100% of tiles with whales, and 94% of tiles containing only water. All model architectures performed well, with learning rate controlling performance more than architecture. While the resolution of commercially-available satellite imagery continues to make whale identification a challenging problem, our approach provides the means to efficiently eliminate areas without whales and, in doing so, greatly accelerates ocean surveys for large cetaceans.

Klíčová slova:

Humpback whales – Machine learning algorithms – Minke whales – Neural networks – Oceans – Surveys – Whales – Right whales


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2019 Číslo 10
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